# XGBoost

Northeast India has lacked a warning system covering landslides, flash floods and road cut-offs together, with monitoring relying on manual reports and fixed rainfall thresholds that rarely reach individual villages. SAMBHV streams IoT telemetry, weather and radar data through one pipeline, uses two-stage hybrid prediction and a three-way vote of physics, AI and ground verification to set alert levels, and runs offline with multilingual query support. Across 311 disaster test cases the platform reports detection F1 of 0.87, an 8% false alarm rate and 30 seconds from threshold trigger to alert.

Northeast India has lacked a warning system covering landslides, flash floods and road cut-offs together, with monitoring relying on manual reports and fixed rainfall thresholds that rarely reach individual villages. SAMBHV streams IoT telemetry, weather and radar data through one pipeline, uses two-stage hybrid prediction and a three-way vote of physics, AI and ground verification to set alert levels, and runs offline with multilingual query support. Across 311 disaster test cases the platform reports detection F1 of 0.87, an 8% false alarm rate and 30 seconds from threshold trigger to alert.

At the Dresden University Cancer Center's MASTERsg precision oncology program, a gradient-boosting model re-reads the raw bin-level copy-number data from TSO500 panel sequencing to find focal PTEN deletions that the standard pipeline misses. TSO500 averages read counts across bins within a gene, so focal events fall short of its fold-change threshold; the model flagged deletions in 33 of 204 samples classified as PTEN wild-type, and 15 of the 20 stainable cases showed absent or reduced PTEN protein.

At the Dresden University Cancer Center's MASTERsg precision oncology program, a gradient-boosting model re-reads the raw bin-level copy-number data from TSO500 panel sequencing to find focal PTEN deletions that the standard pipeline misses. TSO500 averages read counts across bins within a gene, so focal events fall short of its fold-change threshold; the model flagged deletions in 33 of 204 samples classified as PTEN wild-type, and 15 of the 20 stainable cases showed absent or reduced PTEN protein.

On 57 day-ahead test days for a university teaching building, pretrained TimesFM 3.0 with covariates reached 19.97 kWh hourly mean absolute error, about 43% below the weekly seasonal-naive baseline, without training building-specific weights.

On 57 day-ahead test days for a university teaching building, pretrained TimesFM 3.0 with covariates reached 19.97 kWh hourly mean absolute error, about 43% below the weekly seasonal-naive baseline, without training building-specific weights.

Treating undiagnosed hypertension screening as a positive-unlabeled problem in 43,105 Chinese community adults reached a sensitivity of 0.817 and 729 false negatives in an independent test set, 41.1% fewer than standard XGBoost.

Treating undiagnosed hypertension screening as a positive-unlabeled problem in 43,105 Chinese community adults reached a sensitivity of 0.817 and 729 false negatives in an independent test set, 41.1% fewer than standard XGBoost.

Using one year of hourly boarding data from Budapest metro line M4 (216 usable days, 05:00–24:00) restricted to a single station and direction (Kálvin tér toward Kelenföld vasútállomás) and a 6-hour forecast window, the study fairly compared inherently interpretable NeuralProphet with a black-box XGBoost interpreted post hoc via SHAP, finding XGBoost more accurate (MAE 91.0753 vs 230.2803, RMSE 144.7094 vs 363.2115, R-squared 0.9600 vs 0.7896) and SHAP temporal-feature insights consistent with NeuralProphet seasonality components and transport expert knowledge.

Using one year of hourly boarding data from Budapest metro line M4 (216 usable days, 05:00–24:00) restricted to a single station and direction (Kálvin tér toward Kelenföld vasútállomás) and a 6-hour forecast window, the study fairly compared inherently interpretable NeuralProphet with a black-box XGBoost interpreted post hoc via SHAP, finding XGBoost more accurate (MAE 91.0753 vs 230.2803, RMSE 144.7094 vs 363.2115, R-squared 0.9600 vs 0.7896) and SHAP temporal-feature insights consistent with NeuralProphet seasonality components and transport expert knowledge.

The study builds a mechanism-informed machine learning framework that chains a pharmaceutical-knowledge-driven directed acyclic graph, causal structure discovery (PC, NOTEARS, DirectLiNGAM), XGBoost release prediction, explainable AI (SHAP), ATE/CATE intervention-effect estimation, and counterfactual formulation analysis; on a public dataset of 181 release profiles, 3,783 fractional release measurements, and 43 drug-polymer pairs, XGBoost reached test R² = 0.9774, RMSE = 0.0491, and MAE = 0.0339, and correlation and SHAP importance agreed strongly (r = 0.761) while both agreed poorly with intervention-effect estimates (r = 0.132 and −0.021), indicating that variables useful for prediction differ from those that can be manipulated.

The study builds a mechanism-informed machine learning framework that chains a pharmaceutical-knowledge-driven directed acyclic graph, causal structure discovery (PC, NOTEARS, DirectLiNGAM), XGBoost release prediction, explainable AI (SHAP), ATE/CATE intervention-effect estimation, and counterfactual formulation analysis; on a public dataset of 181 release profiles, 3,783 fractional release measurements, and 43 drug-polymer pairs, XGBoost reached test R² = 0.9774, RMSE = 0.0491, and MAE = 0.0339, and correlation and SHAP importance agreed strongly (r = 0.761) while both agreed poorly with intervention-effect estimates (r = 0.132 and −0.021), indicating that variables useful for prediction differ from those that can be manipulated.

Using 1,825,948 chemotherapy appointment records from the Dana-Farber Cancer Institute, this study builds a hierarchical machine learning pipeline that first predicts cancellations and then no-shows, achieving F1-scores of 0.76 and 0.82 and improving minority-class performance by 7–10 points over a single-stage multinomial baseline; it further uses semi-supervised learning to infer no-show reasons from short-notice cancellations (weighted F1 of 0.57 and 0.54) and applies counterfactual simulation to evaluate interventions, finding that standard reminders are less effective than previously reported while provider consistency and commitment-based scheduling can reduce cancellations and no-shows.

Using 1,825,948 chemotherapy appointment records from the Dana-Farber Cancer Institute, this study builds a hierarchical machine learning pipeline that first predicts cancellations and then no-shows, achieving F1-scores of 0.76 and 0.82 and improving minority-class performance by 7–10 points over a single-stage multinomial baseline; it further uses semi-supervised learning to infer no-show reasons from short-notice cancellations (weighted F1 of 0.57 and 0.54) and applies counterfactual simulation to evaluate interventions, finding that standard reminders are less effective than previously reported while provider consistency and commitment-based scheduling can reduce cancellations and no-shows.